1. Field of the Invention
This invention relates to the measurement of brain responses to stimuli in which the presentation of the stimuli is natural to the task and an operator is allowed to move his or her eyes freely in performance of the task.
2. Description of the Related Art
A person's cognitive responses associated with task-relevant brain activity (both conscious and unconscious) may be monitored to study human neurophysiology, perform clinical diagnosis and to detect significant responses to task-relevant or environmental stimuli. In the latter, the detection of such a response may be fed back or used in some manner in conjunction with the task or environment. For example, the response could be used in a classification system to detect and classify visual, auditory or information stimuli, a warning system to detect potential threats, a lie detector system etc. The detection of a significant cognitive response generates a cue that the operator's neurophysiology has responded in a significant way.
Various techniques for monitoring neurophysiological responses as a correlate to cognitive responses include electroencephalography (EEG), pupil dilation and blood flow or oxygenation, each of which has been correlated to changes in neurophysiology. EEG signals represent the aggregate activity of millions of neurons on the cortex and have high time-resolution (capable of detecting changes in electrical activity in the brain on a millisecond-level). Evidence suggests significant amplitude differences between trial-averaged EEG responses triggered by task-relevant stimuli versus trial-averaged EEG responses triggered by neutral stimuli. The benefit of integrating EEG responses across multiple trials is to suppress noise and the task-unrelated background EEG and project out the task-relevant EEG saliently, i.e. improve the signal-to-noise ratio. In EEG systems, electrodes on the scalp measure electrical activity of the brain. The EEG signals contain data and patterns of data associated with brain activity. A classifier is used to analyze the EEG signals to infer the existence of certain brain states.
In US Pub No. 2007/0185697 entitled “Using Electroencephalograph Signals for Task Classification and Activity Recognition” Tan describes a trial-averaged spatial classifier for discriminating operator performed tasks for EEG signals. Recent advances in adaptive signal processing have demonstrated significant single trial detection capability by integrating EEG data spatially across multiple channels of high density EEG sensors (L. Parra et al, “Single trial Detection in EEG and MEG: Keeping it Linear”, Neurocomputing, vol. 52-54, June 2003, pp. 177-183, 2003 and L. Parra et al, “Recipes for the Linear Analysis of EEG”, NeuroImage, 28 (2005), pp. 242-353)). The linear (LDA) classifier provides a weighted sum of all electrodes over a predefined temporal window as a new composite signal that serves as a discriminating component between responses to target versus distracter stimuli.
A rapid serial visual presentation (RSVP) system for triaging imagery is an example of a single-trial EEG system (A. D. Gerson et al “Cortical-coupled Computer Vision for Rapid Image Search”, IEEE Transaction on Neural Systems and Rehabilitation Engineering, June 2006) for stimuli in a constrained environment in that both the presentation of stimuli and the analyst's viewing are carefully controlled. Image clips are displayed to the analyst at a rate of approximately 10 per second and a multi-channel LDA classifier is employed to classify the brain response to the presentation of each image. If a significant response is indicated, the system flags the image clip for closer inspection.